Level: Beginner to Intermediate
We’re buried in data and everyone’s discussing the power of analytics, the single version of the truth, and self-service. Unfortunately, your current approach is to react the latest challenge, problem, or surprise. You need a plan—and you need to learn how to build one quickly.
Our analytics environment is a collection of data, tools, and technologies. We’ve not enabled business analysis; we’re training everyone to become a data developer. We need an environment that has a cohesive plan that delivers easy-to-find data, enables self-service, aligns with the company’s strategy, and supports the individual’s responsibilities.
In this session, Evan Levy will review the phases of developing an analytics/DW roadmap and the tactical activities of planning, scoping, and organizing analytics and data initiative. We’ll discuss the different audiences, the approach of scoping and identifying needs, and even provide a framework for capturing and managing these details. We’ll also talk about the organizational changes required to support self-service and other user-centric data initiatives.
You Will Learn
- The common issues and needs that reflect the necessity for an Analytics/Data Plan
- Activities for gathering user needs and requirements
- Managing and prioritizing needs using an Analytics Portfolio
- The strengths and challenges of different development methods (agile, iterative, waterfall, etc.)
- Identifying the core technology components of the environment
- Organizational roles and responsibilities involved in an analytics and data initiative
- BI/DW project managers
- Business analysts
- Data analysts
- Data architects
- Data warehouse developers
- BI/analytics stakeholder
This course does NOT require Data Strategy I or II
In this hands-on class you will learn the fundamentals of applying logistic regression to business data using Excel. Logistic regression is the most common predictive model used to answer business questions like “how likely is a customer to churn?”
Excel provides all the functionality needed for crafting logistic regression models on par with models from programming languages like R and Python.
After learning about the types of business problems that might benefit from logistic regression analyses, we will teach you how logistic regression can answer interesting business questions such as:
- Given a customer’s behavior, how likely are they to convert to paying?
- What factors are most important in determining if a customer will churn?
- Does the interaction of certain product characteristics affect the likelihood of a warranty claim?
Whether you are an Excel-based analyst looking to improve your skills or a manager interested in understanding the logistic regression models produced by analytics teams, this is the class for you.
Want to know the best part? No programming or difficult mathematics are required!
You Will Learn
- The type of business problems where logistic regression can be useful
- The basics of probabilities and odds ratios
- Templates that streamline logistic regression analyses in Excel
- Using Excel’s Solver to train simple logistic regression models
- Using Excel’s Solver to train multiple logistic regression models
- Interpreting logistic regression models in terms of business drivers
- Evaluating the effectiveness of your logistic regression models
- How to communicate your insights effectively
- Additional resources to extend your learning
- Business/data analysts
- Database developers
- BI/report developers
- Anyone interested in using logistic regression for analyzing business data
No skills in programming or statistics are required.
Participants should download and install the following prior to the event:
Microsoft Excel 2016 or later
Instructions will be emailed to registrants prior to the event to prepare your laptop BEFORE the conference. There is no time allotted in class for laptop preparation.